Dynamic Multi-Fault Diagnosis Based Root Cause Tracing for Liquid Storage Tanks Assembly Production Lines

• DOI: 10.20944/preprints202502.2259.v1
• Publication Date: 2025-03-03T03:06:51Z
• Authors: You Teng; Donghui Li; Hongkai Xue; Yunkai Zhou; Kefu Wang; Qi Wu

Tracing the root causes of defective products in liquid storage tank (LST) production poses a formidable challenge due to the complexity and coupling of the processes within the production. In this paper, the problem of tracing the root cause of defective LST products which is caused by process parameter deviations or human operation errors during production is studied. A root cause tracing method that is based on the dynamic multi-fault diagnosis (DMFD) framework is proposed. First, a factorial hidden Markov model (FHMM) is established to depict the state-transition process of the LST product, where its status changes over time and across production processes. This is achieved by considering the product state at each production process as a hidden state and the outcomes of each inspection process as an observation state. Then, the Viterbi algorithm is employed to solve the hidden state transition matrix and diagnostic matrix within the framework of the FHMM. Finally, experimental verification is carried out on a real LST assembly production line, and the influence of imperfect testing on the model accuracy is also considered. Experimental results show that the proposed method achieves a 100% accuracy rate for root cause tracing of three typical quality issues.